Sang Choe

Current: PhD, Carnegie Mellon University
E-mail: sangkeuc [at]
Office: GHC 5511

LinkedIn | Google Scholar | GitHub | CV


I am Sang Choe, a final year CS PhD student in Language Technologies Institute at Carnegie Mellon University, advised by Eric Xing.

My research focuses on gradient-based optimization with the belief that gradient is a key to understand various aspects of modern AI/ML, including generalization, uncertainty, and interpretability. I also strive to understand gradient-based optimization from the computer systems viewpoint, as, at the end of the day, what runs these AI/ML algorithms are computers, requiring most practically useful algorithms to be highly compatible with underlying systems. Last but not least, I aim to turn my research into highly scalable, interoperable, easy-to-use softwares.

In technical terms, my research lies in the intersection of:

◆  systems for machine learning
◆  machine learning debugging/auditing (data, weight, algorithm)
◆  automated machine learning
◆  machine learning software

Previously, I completed MS in Language Technologies at Carnegie Mellon University under the guidance of Jaime Carbonell. Before that, I earned BS in Electrical Computer Engineering & Mathematics (double major) from Seoul National University. I had also spent time as a research intern at Microsoft in 2021.


◆  Betty got accepted as an oral presentation at ICLR 2023!
◆  Released Betty, a PyTorch library for easy, scalable, and modular meta-learning!
◆  Received CMU Presidential Scholarship for PhD!
◆  Started research internship at Microsoft in Summer 2021!
◆  Won Jay Lepreau Best Paper Award in OSDI 2021!

Making Scalable Meta Learning Practical NeurIPS, 2023
Sang Keun Choe, Sanket Vaibhav Mehta, Hwijeen Ahn, Willie Neiswanger, Pengtao Xie, Emma Strubell, and Eric Xing

Betty: An Automatic Differentiation Library for Multilevel Optimization [code] ICLR, 2023
Sang Keun Choe, Willie Neiswanger, Pengtao Xie, and Eric Xing
Oral (1.8% acceptance rate)

Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning [code] OSDI, 2021
Aurick Qiao, Sang Keun Choe, Suhas Subramanya, Willie Neiswanger, Qirong Ho, Hao Zhang, Greg Ganger, Eric Xing
🏆 Jay Lepreau Best Paper Award

On Orthogonal Jacobian Regularization in Deep Neural Networks SEDL Workshop @ NeurIPS, 2019
Sang Keun Choe*, Hosan Jeong*, Jaime Carbonell

On Leveraging the Visual Modality for Neural Machine Translation INLG, 2019
Vikas Raunak*, Sang Keun Choe*, Quanyang Lu*, Yi Xu*, Florian Metze

Audio Cover Song Identification using Convolutional Neural Network ICASSP, 2017
Sungkyung Chang, Juheon Lee, Sang Keun Choe, Kyogu Lee


CMU Presidential Scholarship, Carnegie Mellon University
Jay Lepreau Best Paper Award, OSDI
Kwanjeong Scholarship for Graduate Study, Kwanjeong Educational Foundation
Best Undergraduate Engineering Student Award, Seoul National University
Presidential Scholarship for Science and Engineering Study, Korea Student Aid Foundation
Gold Medal, Korea Collegiate Mathematical Competition
Silver Medal, Korea Mathematical Olympiad

In my spare time, I serve as a dog walker for my three-year-old bernedoodle, Betty.

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